AI Academy

For data scientists and engineers working with GPUs, achieving optimal performance is crucial. However, launching frequent, short-running kernels can introduce overhead from CPU management. This is where CUDA Graphs come in. Introduced in CUDA 10, CUDA Graphs offer a powerful way to streamline GPU workloads and squeeze out extra performance. Traditionally, launching multiple GPU kernels involves significant CPU overhead. Each launch incurs setup costs that can dominate execution time, especially for short-running kernels. CUDAGraphs tackle this by allowing you to define your workflow as a graph, specifying the sequence of operations on the GPU. This graph can then be launched with a single CPU call, significantly reducing overhead.

What are CUDA Graphs?

Imagine a flowchart representing the different stages of your program’s execution on the GPU. CUDA Graphs allow you to capture this flow and encode it as a graph data structure. This graph includes not only the computations (kernels) you want to run but also the data dependencies between them.

Think of it like this: Imagine building a complex toy car out of Lego. Traditionally, you’d fetch each piece one by one and snap them together. With CUDAGraphs, you lay out all the pieces in the correct order beforehand, then follow the plan to assemble the car much faster.

Benefits of Using CUDA Graphs

• Reduced Launch Overhead: Launching individual kernels incurs CPU overhead. By grouping them into a graph, you launch the entire workflow with a single CPU call, significantly reducing this overhead for short-running kernels.
• Improved Performance: Less CPU involvement translates to more GPU utilization, leading to faster execution times for your program.
• Simplified Code: CUDA Graphs provide a clear and concise way to represent complex workflows, improving code readability and maintainability.

Who can Benefit from CUDA Graphs?

CUDA Graphs are particularly valuable for applications with frequently executed sequences of GPU operations. This includes:

• Deep Learning Inference: 
  • Multiple Processing Stages: Deep learning models often consist of several interconnected layers, each performing specific computations. Traditionally, launching kernels for each layer incurs overhead.
  • Streamlined Execution with CUDAGraphs: CUDAGraphs allow you to define the entire inference process as a graph. This graph specifies the sequence of calculations across different layers. When launched, the GPU executes the entire flow efficiently, eliminating the need for multiple kernel launches and reducing CPU overhead. This translates to faster inference times
• Scientific Simulations:
  • Repeated Calculations with Dependencies: Scientific simulations frequently involve repetitive calculations with well-defined dependencies. For example, simulating fluid flow might involve calculations for pressure followed by velocity, each dependent on the other.
  • Capturing Workflows as Graphs: CUDAGraphs shine here. You can define the entire simulation workflow within the graph, specifying the order of calculations and any data dependencies between them. This pre-defined structure allows the GPU to optimize execution and minimize overhead associated with launching individual kernels for each step. The result? A significant boost in simulation speed.

Getting Started with CUDA Graphs

Utilizing CUDAGraphs involves creating a graph object, defining the execution order by adding kernels and data transfers, and then launching the executable graph on the GPU. The CUDA programming guide provides detailed information and code examples to get you started https://developer.nvidia.com/blog/cuda-graphs/.